def __init__(self):
super(SoftmaxHeteroscedasticLoss, self).__init__()
self.adf_softmax = adf.Softmax(dim=1,
keep_variance_fn=keep_variance_fn)
def train_epoch(self,
train_loader,
model,
criterion,
optimizer,
epoch,
evaluator,
scheduler,
color_fn,
report=10,
show_scans=False):
losses = AverageMeter()
acc = AverageMeter()
iou = AverageMeter()
hetero_l = AverageMeter()
update_ratio_meter = AverageMeter()
# empty the cache to train now
if self.gpu:
torch.cuda.empty_cache()
# switch to train mode
model.train()
end = time.time()
for i, (in_vol, proj_mask, proj_labels, _, path_seq, path_name, _, _,
_, _, _, _, _, _, _) in enumerate(train_loader):
# measure data loading time
self.data_time_t.update(time.time() - end)
if not self.multi_gpu and self.gpu:
in_vol = in_vol.cuda()
#proj_mask = proj_mask.cuda()
if self.gpu:
proj_labels = proj_labels.cuda().long()
# compute output
if self.uncertainty:
output = model(in_vol)
output_mean, output_var = adf.Softmax(
dim=1, keep_variance_fn=keep_variance_fn)(*output)
hetero = self.SoftmaxHeteroscedasticLoss(output, proj_labels)
loss_m = criterion(output_mean.clamp(min=1e-8),
proj_labels) + hetero + self.ls(
output_mean, proj_labels.long())
hetero_l.update(hetero.mean().item(), in_vol.size(0))
output = output_mean
else:
output = model(in_vol)
loss_m = criterion(torch.log(output.clamp(min=1e-8)),
proj_labels) + self.ls(
output, proj_labels.long())
optimizer.zero_grad()
if self.n_gpus > 1:
idx = torch.ones(self.n_gpus).cuda()
loss_m.backward(idx)
else:
loss_m.backward()
optimizer.step()
# measure accuracy and record loss
loss = loss_m.mean()
with torch.no_grad():
evaluator.reset()
argmax = output.argmax(dim=1)
evaluator.addBatch(argmax, proj_labels)
accuracy = evaluator.getacc()
jaccard, class_jaccard = evaluator.getIoU()
losses.update(loss.item(), in_vol.size(0))
acc.update(accuracy.item(), in_vol.size(0))
iou.update(jaccard.item(), in_vol.size(0))
# measure elapsed time
self.batch_time_t.update(time.time() - end)
end = time.time()
# get gradient updates and weights, so I can print the relationship of
# their norms
update_ratios = []
for g in self.optimizer.param_groups:
lr = g["lr"]
for value in g["params"]:
if value.grad is not None:
w = np.linalg.norm(value.data.cpu().numpy().reshape(
(-1)))
update = np.linalg.norm(
-max(lr, 1e-10) * value.grad.cpu().numpy().reshape(
(-1)))
update_ratios.append(update / max(w, 1e-10))
update_ratios = np.array(update_ratios)
update_mean = update_ratios.mean()
update_std = update_ratios.std()
update_ratio_meter.update(update_mean) # over the epoch
if show_scans:
# get the first scan in batch and project points
mask_np = proj_mask[0].cpu().numpy()
depth_np = in_vol[0][0].cpu().numpy()
pred_np = argmax[0].cpu().numpy()
gt_np = proj_labels[0].cpu().numpy()
out = Trainer.make_log_img(depth_np, mask_np, pred_np, gt_np,
color_fn)
mask_np = proj_mask[1].cpu().numpy()
depth_np = in_vol[1][0].cpu().numpy()
pred_np = argmax[1].cpu().numpy()
gt_np = proj_labels[1].cpu().numpy()
out2 = Trainer.make_log_img(depth_np, mask_np, pred_np, gt_np,
color_fn)
out = np.concatenate([out, out2], axis=0)
cv2.imshow("sample_training", out)
cv2.waitKey(1)
if self.uncertainty:
if i % self.ARCH["train"]["report_batch"] == 0:
print(
'Lr: {lr:.3e} | '
'Update: {umean:.3e} mean,{ustd:.3e} std | '
'Epoch: [{0}][{1}/{2}] | '
'Time {batch_time.val:.3f} ({batch_time.avg:.3f}) | '
'Data {data_time.val:.3f} ({data_time.avg:.3f}) | '
'Loss {loss.val:.4f} ({loss.avg:.4f}) | '
'Hetero {hetero_l.val:.4f} ({hetero_l.avg:.4f}) | '
'acc {acc.val:.3f} ({acc.avg:.3f}) | '
'IoU {iou.val:.3f} ({iou.avg:.3f}) | [{estim}]'.format(
epoch,
i,
len(train_loader),
batch_time=self.batch_time_t,
data_time=self.data_time_t,
loss=losses,
hetero_l=hetero_l,
acc=acc,
iou=iou,
lr=lr,
umean=update_mean,
ustd=update_std,
estim=self.calculate_estimate(epoch, i)))
save_to_log(
self.log, 'log.txt', 'Lr: {lr:.3e} | '
'Update: {umean:.3e} mean,{ustd:.3e} std | '
'Epoch: [{0}][{1}/{2}] | '
'Time {batch_time.val:.3f} ({batch_time.avg:.3f}) | '
'Data {data_time.val:.3f} ({data_time.avg:.3f}) | '
'Loss {loss.val:.4f} ({loss.avg:.4f}) | '
'Hetero {hetero.val:.4f} ({hetero.avg:.4f}) | '
'acc {acc.val:.3f} ({acc.avg:.3f}) | '
'IoU {iou.val:.3f} ({iou.avg:.3f}) | [{estim}]'.format(
epoch,
i,
len(train_loader),
batch_time=self.batch_time_t,
data_time=self.data_time_t,
loss=losses,
hetero=hetero_l,
acc=acc,
iou=iou,
lr=lr,
umean=update_mean,
ustd=update_std,
estim=self.calculate_estimate(epoch, i)))
else:
if i % self.ARCH["train"]["report_batch"] == 0:
print(
'Lr: {lr:.3e} | '
'Update: {umean:.3e} mean,{ustd:.3e} std | '
'Epoch: [{0}][{1}/{2}] | '
'Time {batch_time.val:.3f} ({batch_time.avg:.3f}) | '
'Data {data_time.val:.3f} ({data_time.avg:.3f}) | '
'Loss {loss.val:.4f} ({loss.avg:.4f}) | '
'acc {acc.val:.3f} ({acc.avg:.3f}) | '
'IoU {iou.val:.3f} ({iou.avg:.3f}) | [{estim}]'.format(
epoch,
i,
len(train_loader),
batch_time=self.batch_time_t,
data_time=self.data_time_t,
loss=losses,
acc=acc,
iou=iou,
lr=lr,
umean=update_mean,
ustd=update_std,
estim=self.calculate_estimate(epoch, i)))
save_to_log(
self.log, 'log.txt', 'Lr: {lr:.3e} | '
'Update: {umean:.3e} mean,{ustd:.3e} std | '
'Epoch: [{0}][{1}/{2}] | '
'Time {batch_time.val:.3f} ({batch_time.avg:.3f}) | '
'Data {data_time.val:.3f} ({data_time.avg:.3f}) | '
'Loss {loss.val:.4f} ({loss.avg:.4f}) | '
'acc {acc.val:.3f} ({acc.avg:.3f}) | '
'IoU {iou.val:.3f} ({iou.avg:.3f}) | [{estim}]'.format(
epoch,
i,
len(train_loader),
batch_time=self.batch_time_t,
data_time=self.data_time_t,
loss=losses,
acc=acc,
iou=iou,
lr=lr,
umean=update_mean,
ustd=update_std,
estim=self.calculate_estimate(epoch, i)))
# step scheduler
scheduler.step()
return acc.avg, iou.avg, losses.avg, update_ratio_meter.avg, hetero_l.avg